Opportunistic Reuse of Spatial-Temporal Resources in Multi-User ISAC Systems for Value of Service Maximization
Bibliographic record
Abstract
Supporting rapidly growing industrial applications with complex heterogeneous service requests exacerbates the radio resource shortage, posing a perpetual challenge for future networks. Emerging beyond-communication technologies for providing concurrent services, such as integrated sensing and communication (ISAC), further complicate resource allocation due to the uneven and non-uniform distribution of heterogeneous service demands as well as the growing network conflict among concurrent services. To tackle these issues, this paper propose an opportunistic spatiotemporal resource reuse scheme that optimally improve resource utilization efficiency by leveraging the disparities in resource utilization capabilities among users across both spatial and temporal domains. To enhance the heterogeneous service provisioning for different users, a Value of Service (VoS) metric, adopted to evaluate resource allocation performance per user per unit space, is optimized through a clustering-based resource-sharing strategy. To reduce mutual interference among users, the base station utilizes a clustering process that considers each user’s physical location and service request. In each cluster, we derive an analytical solution for communication resource allocation and use a many-to-many matching-based algorithm for assigning the sensing subchannels. The numerical simulation results demonstrate enhanced resource utilization efficiency of our proposed scheme compared with other benchmarks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".